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Record W2111298553 · doi:10.1080/07388550600840525

Biotechnological Methods to Accelerate Cheddar Cheese Ripening

2006· review· en· W2111298553 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCritical Reviews in Biotechnology · 2006
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsAgriculture and Agri-Food CanadaMcGill University
Fundersnot available
KeywordsRipeningCheese ripeningFood scienceContext (archaeology)ChemistryCheesemakingFlavorLipolysisBiologyBiochemistry

Abstract

fetched live from OpenAlex

Cheese is one of the dairy products that can result from the enzymatic coagulation of milk. The basic steps of the transformation of milk into cheese are coagulation, draining, and ripening. Ripening is the complex process required for the development of a cheese's flavor, texture and aroma. Proteolysis, lipolysis and glycolysis are the three main biochemical reactions that are responsible for the basic changes during the maturation period. As ripening is a relatively expensive process for the cheese industry, reducing maturation time without destroying the quality of the ripened cheese has economic and technological benefits. Elevated ripening temperatures, addition of enzymes, addition of cheese slurry, attenuated starters, adjunct cultures, genetically engineered starters and recombinant enzymes and microencapsulation of ripening enzymes are traditional and modern methods used to accelerate cheese ripening. In this context, an up to date review of Cheddar cheese ripening is presented.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.150
GPT teacher head0.442
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it